计算机辅助骨年龄估计,使用基于神经网络的甲手腕骨和甲手腕关节的尺寸
Abdolaziz Haghnegahdar1, Hamid Reza Pakshir2, Mojtaba Zandieh3
1Dept. of Oral and Maxillofacial Radiology, School of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran.
Journal of dentistry (Shiraz, Iran)
|March 28, 2024
概括
这项研究表明,经过训练的神经网络可以使用定量骨数据准确估计骨年龄. 人工智能模型显示出高可靠性,可与专家放射科医生相美,用于评估生物发育.
科学领域:
- 儿科内分泌学 儿科内分泌学
- 放射学 放射学是一门学科.
- 人工智能在医学中的应用
背景情况:
- 骨年龄评估对于评估生物发育至关重要,往往超过时间学年龄的准确性.
- 像Greulich和Pyle Atlas这样的传统方法在定量分析中带来了挑战.
- 最近的研究突出显示了甲手腕和甲手腕角尺寸与骨年龄之间的相关性.
研究的目的:
- 评估训练有素的神经网络用于骨龄估计的准确性和可靠性.
- 利用定量数据,包括骨尺寸,以改善骨年龄的评估.
- 将人工智能驱动的骨龄估计与传统的放射性评估进行比较.
主要方法:
- 一项涉及304名受试者的回顾性横截面研究.
- 在 MATLAB 中开发和利用神经网络来确定骨年龄.
- 使用了定量特征,包括时间表年龄,身高,体重和骨尺寸.
主要成果:
- 神经网络在骨年龄估计方面表现出高准确性,与放射科医生的评估相比没有显著差异 (p > 0.1).
- 在软件和放射科医生的骨年龄评估之间观察到强烈的相关性 (ICC = 0.986-0.990).
- 人工智能模型在男性和女性受试者中都被证明是可靠的.
结论:
- 用定量骨和关节尺寸训练神经网络可以为骨年龄估计提供可接受的准确性.
- 基于人工智能的方法为骨年龄评估提供了可靠和潜在的更有效的方法.
- 这项技术有望提高儿科生长和发育评估.
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